Assignment-3

1 Data Management

1.1 Reshaping and Merging the datasets

Read four datasets from github repository into four objects named income, popul, life and countries.

Reshaped the dataset income into a longitudinal dataset which contains only three columns country, year and income by using gather and mutate functions from dplyr package.

In the same manner, reshaped the datasets popul and life into longitudinal data such that the resulting datasets contains only three respective columns.

Then, merged the datasets life and income into a new dataset LifeExpIncom

Merged the above dataset LifeExpIncom with dataset countries so that the final data set has information about income, life expectancy, and country region.

Finally, Merged the previous resulting data set with dataset popul so that the final data set has information about income, life expectancy, population size, and country region, and named it as InLEPopCtry

Applied filter() function, to select the data for the year 2015 inorder to create the interactive scatter plot for the year 2015.

income <- read.csv("https://raw.githubusercontent.com/VigneshReddy79/STA553-DataViz/main/Datasets/income_per_person.csv")
popul <- read.csv("https://raw.githubusercontent.com/VigneshReddy79/STA553-DataViz/main/Datasets/population_total.csv")
life <- read.csv("https://raw.githubusercontent.com/VigneshReddy79/STA553-DataViz/main/Datasets/life_expectancy_years.csv")
countries <- read.csv("https://raw.githubusercontent.com/VigneshReddy79/STA553-DataViz/main/Datasets/countries_total.csv")

income.new <- income %>%
  gather(key = "Year", value = "Income", -geo, na.rm=TRUE) %>%
  mutate(Country = geo, Year = substr(Year,2,5)) %>%
  select(-geo)
#head(income.new)

life.new <- life %>%
  gather(key = "Year", value = "Life", -geo, na.rm=TRUE) %>%
  mutate(Country = geo, Year = substr(Year,2,5)) %>%
  select(-geo)
#head(life.new)

popul.new <- popul %>%
  gather(key = "Year", value = "population", -geo, na.rm=TRUE) %>%
  mutate(Country = geo, Year = substr(Year,2,5)) %>%
  select(-geo)
#head(popul.new)

LifeExpIncom <- merge(income.new, life.new, by = c("Country","Year") , all.x = FALSE)
LifeIncomCoun <- merge(LifeExpIncom, countries, by.x = "Country", by.y = "name", all.x = FALSE)
LifeIncomCoun <- select(LifeIncomCoun, Year, Country, region, Income, Life)
InLEPopCtry <- merge(LifeIncomCoun, popul.new, by = c("Country","Year"), all.x = FALSE)

rm(countries, income, income.new, life, life.new, LifeExpIncom,LifeIncomCoun, popul, popul.new)
df2015 <- filter(InLEPopCtry, Year == 2015)

2 Interactive Scatter plot

An Interactive scatter plot is created to show the association between the Avg Income and Life Expectancy for the year 2015.

This scatter plot is created by using the plot_ly function from plotly package

The instructions followed to construct the below interactive scatter plot are

1. Set the point size to be proportional to the population size.
2. Use different colors for different countries.
3. Choose an appropriate transparency level so that overlapped points can be viewed.
4. Choose an appropriate color to highlight the point boundary so that partially overlapped points can be easily distinguished.
5. Included the country name and population size in the hover text.

df2015 <- mutate(df2015, population_m = population/1000000)

fig1 <- plot_ly( data = df2015,
                 x = ~Income,
                 y = ~Life,
                 marker = list(line = list(color = 'black',
                                           width = 0.75)),
                 color = ~Country,
                 size = ~population_m,
                 sizes = c(50,1000),
                 type = "scatter",
                 mode = "markers",
                 alpha = 0.75,
                 hoverinfo = 'text',
                 text = paste("<br><b>Income: $</b>", df2015$Income,
                              "<br><b>Life: </b>", df2015$Life,
                              "<br><b>Population in millions: </b>", df2015$population_m,
                              "<br><b>Country: </b>", df2015$Country ) ) %>%
        layout( showlegend = FALSE,
                plot_bgcolor ='#E2E2E2',
                title = list( text = "<b>Relationship between Avg Income and Life Expectancy in 2015</b>",
                              font = list( size = 20,
                                           color = "darkred") ),
                xaxis = list( title = "<b>Avg Income per person </b>($)",
                              zerolinewidth = 1.5,
                              gridcolor = 'white'),
                yaxis = list( title = "<b>Life Expectancy </b>(years)",
                              zerolinewidth = 1.5,
                              gridcolor = 'white'),
                margin = list( b = 15, l = 25, t = 85, r = 25) )

fig1

3 Animated Scatter plot

An Animated scatter plot is created to display the change in relationship between Avg income and life expectancy over the years. (considered the years from 1950 to 2018)

This animated plot is created by using aspects/functions of gganimate and ggplot packages. Also, this animated plot is created by considering the below aspects.

1. Set the point size to be proportional to the population size
2. Use different colors for different regions.
3. Choose an appropriate transparency level so that overlapped points can be viewed.
4. Choose an appropriate color to highlight the point boundary so that partially overlapped points can be easily distinguished.

InLEPopCtry$Year <- as.numeric(InLEPopCtry$Year)
df50_18 <- filter(InLEPopCtry, Year >= 1950 & Year <= 2018)

fig2 <- ggplot(data = df50_18, mapping = aes( x = Income,
                                              y = Life,
                                              size = population,
                                              color = Country)) +
        geom_point(alpha = 0.5, show.legend = FALSE) +
        scale_color_viridis_d() + 
        scale_size(range = c(2, 12)) +
        scale_x_log10() +
        scale_y_log10() +
        labs( title = list( text = paste("Animated Scatter Plot between Avg Income and Life                                              Expectancy", "from 1950 to 2018", sep = "\n"),
                            color = "darkred"),
              subtitle = 'Year: {frame_time}',
              x = "Avg Income ($)",
              y = "Life expectancy (years)") +
        transition_time(as.integer(Year)) +
        ease_aes('linear')

animate(fig2, renderer = gifski_renderer(), rewind = TRUE)

anim_save("animated_plot.gif", fig2, path = "D:/OneDrive - West Chester University of PA/Documents/Spring'22/STA553-DataViz/git/Images")

4 Interactive Choropleth map

Firstly, read the data from github and assigned it to a object named df

From the above dataset, a sample of 500 observations was randomly selected and assigned it to another object named df2

Then using plot_geo function from plotly package, created the Choropleth map showing a random sample of 500 gas stations in the US.

The information included in the hover: State, County, Address and the ZIP code of the gas station.

df <- read.csv("https://raw.githubusercontent.com/VigneshReddy79/STA553-DataViz/main/Datasets/Gas_stations_POC.csv")

set.seed(71725)
df_500 <- sample_n(df, 500)

g <- list( scope = 'usa',
           projection = list(type = 'albers usa'),
           showland = TRUE,
           landcolor = toRGB("gray95"),
           subunitcolor = toRGB("gray85"),
           countrycolor = toRGB("gray85"),
           countrywidth = 0.5,
           subunitwidth = 0.5 )

l <- list( font = list( family = "sans-serif",
                        size = 12,
                        color = "#000"),
           bgcolor = "#E2E2E2",
           bordercolor = "#FFFFFF",
           borderwidth = 2 )

fig3 <- plot_geo( data = df_500, x = ~xcoord, y = ~ycoord) %>%
        add_markers( text = ~paste("<br><b>State: </b>", STATE,
                                   "<br><b>County: </b>", county,
                                   "<br><b>Address: </b>", ADDRESS,
                                   "<br><b>Zip code: </b>", ZIPnew),
                     color = ~description,
                     colors = c("springgreen3","dodgerblue3"),
                     alpha = 0.5,
                     symbol = "circle", 
                     #marker = list( line = list( color = 'black', width = 0.5)),
                     size = I(30),
                     hoverinfo = "text") %>%
        layout( title = list( text = "<b>500 Random Gas stations in the United States</b>",
                              font = list( size = 20,
                                           color = "darkred" )),
                legend = l,
                margin = list( b = 15, l = 25, t = 85, r = 25),
                geo = g )

fig3